Microarray experiments require careful planning and choice of analysis tools in order to get the most out of the data generated, especially considering the associated significant cost and effort. Microarray experiments also require careful documentation, often residing in local databases and/or submitted to public repositories. An often bewildering assortment of choices is available for experimental design, data preprocessing, data analysis (e.g., differential gene expression, classification), and data management. This unit covers the basic steps and common applications for planning, data processing, and data management of microarray experiments, and provides guidance to making choices based on the goals and practical realities of the experiment, as well as the authors' experience in this area.